Payers and providers will see artificial intelligence, care coordination, cybersecurity, data analytics, digital therapeutics and telehealth drive the digital health market in 2020. The study by Dublin-based Research and Markets, entitled, "Global Digital Health Outlook, 2020," says digital health "is a growing but complex market," with an expected compound annual growth rate of 12 percent through 2023. Payers should look to digital solutions to boost the quality and efficiency of healthcare, authors of the study say. In addition, digital health solutions are expected to offer "great promise" for new care delivery models and expanded access. For the study, analysts looked at the recurring themes visible across all global markets, including an aging population, a rising prevalence of chronic diseases, anticipated medical staff shortages, long wait times to get care, universal concerns about the quality of care, and the need for a common technical platform.

Healthcare workers can often feel like a very small cog in the very big machine that is the healthcare system. From doctors and nurses to administration staff, there is little doubt that with an increasing ageing population in both Australia and New Zealand (ANZ), healthcare workers are expected to find ways to do more with less. Recent research suggests that ANZ-based healthcare workers are very open to embracing new technologies in the workplace, including artificial intelligence (AI) and machine learning, especially if it can help to make their jobs more efficient. This is according to findings from a survey by technology provider Genesys. The survey, which focused on employee attitudes about the implementation of advanced technologies in the workplace, found almost 70% of respondents from the healthcare sector believe technology makes them more efficient at work, and 35% responded saying AI had already made a positive impact on their job.

Often managers assume that because the software is devoid of emotions (unlike humans), using them would mean the complete removal of personally-motivated bias from the hiring process. Any application of AI (Artificial Intelligence) or Machine Learning (ML) learns from the existing data fed to it. This raises concerns such as an amplification of pre-existing bias in the data that have made hiring managers approach them with caution. Amazon's experiment into an AI-based recruiting system was scrapped after it started penalizing resumes that included the term "women's" or names of women's colleges. Essentially, the software taught itself to prefer resumes of male candidates over those of female candidates.

From Jeopardy winners and Go masters to infamous advertising-related racial profiling, it would seem we have entered an era in which artificial intelligence developments are rapidly accelerating. But a fully sentient being whose electronic "brain" can fully engage in complex cognitive tasks using fair moral judgement remains, for now, beyond our capabilities. Unfortunately, current developments are generating a general fear of what artificial intelligence could become in the future. Its representation in recent pop culture shows how cautious – and pessimistic – we are about the technology. The problem with fear is that it can be crippling and, at times, promote ignorance.

The technology of Artificial Intelligence has been here for a long time now and is improving itself every year from the past few decades. As a result of such long due advancements and progress, AI has become exponentially mature to impact the world for the greater good. Several companies are actively working with AI to bring about digital transformation in the ecosystem they work in or live in. With industry-wide adoption, AI is enabling a variety of big ideas to transform into interesting innovations. Here is the list of top 5 AI-centric innovative products launched in 2019 that will change the landscape of technology future across various sectors.

Gartner considers the following AI technologies to be on the rise and part of the Innovation Trigger phase of the AI Hype Cycle. AI Marketplaces, Reinforcement Learning, Decision Intelligence, AI Cloud Services, Data Labeling, and Annotation Services, and Knowledge Graphs are now showing signs of potential technology breakthroughs as evidence by early proof-of-concept stories. Technologies in the Innovation Trigger phase of the Hype Cycle often lack usable, scalable products with commercial viability not yet proven. Smart Robots and AutoML are at the peak of the Hype Cycle in 2019. In contrast to the rapid growth of industrial robotics systems that adopted by manufacturers due to the lack of workers, Smart Robots are defined by Gartner as having electromechanical form factors that work autonomously in the physical world, learning in short-term intervals from human-supervised training and demonstrations or by their supervised experiences including taking direction form human voices in a shop floor environment.

Schneider Electric recently debuted factory upgrades that it claims make its Lexington, KY, factory the first smart factory in the United States. The factory uses technology like augmented reality to give workers live operational data about machines on the floor. And the change resulted in significant improvements in efficiency: a 90% reduction of paperwork and a 20% reduction in the mean repair time. Smart factory owners looking to make the next leap forward in the digital industrial revolution are turning to advanced technology like artificial intelligence and big data. Here are six smart factory developments we're likely to see within the next year.

Modern artificial intelligence lacks a strong theoretical basis, and so it's often a shrug of the shoulders why it works at all (or, oftentimes, doesn't entirely work). One of the deepest mysteries of deep learning is one of its most brilliant successes, what's known as stochastic gradient descent. Stochasticity, the process of randomly picking out examples of data, has yielded breakthroughs in image recognition and other deep learning tasks. And now, one computer chip company thinks they may have a kind of machine for stochasticity, a chip whose power comes from randomness. It might not lead to a theory of why machine learning works, but it might lead to knew breakthroughs in what stochasticity can achieve.

Today HPE announced announced the deployment of a new supercomputer at the MIT Lincoln Laboratory Supercomputing Center for compute-intensive AI applications and bolstering research across engineering, science, and medicine. Called TX-GAIA (Green AI Accelerator), the new supercomputer converges HPC and AI to support workloads such as modeling and simulation and perform complex deep neural networks (DNN) and other machine learning training. It is based on the HPE Apollo 2000 system, which is purpose-built for HPC and optimized for AI, by integrating the latest Intel Xeon Scalable processors and NVIDIA GPU accelerators. At the MIT Lincoln Laboratory Supercomputing Center, our mission is to solve the nation's hardest technical challenges by advancing computationally intensive science, engineering, and medicine," said Jeremy Kepner, head and founder, at MIT Lincoln Laboratory Supercomputing Center (LLSC). "By collaborating with HPC leaders like HPE, we are expanding technical capabilities to run emerging AI workloads in our supercomputer and accelerate innovation." The new supercomputer has a measured performance of 4.725 Petaflops and will be used to support research projects that will fuel innovation in weather forecasting, medical data analysis, autonomous systems, synthetic DNA design, and new materials and devices. Additionally, the MIT Lincoln Laboratory Supercomputing Center's new system gets an AI performance boost, as measured by the computing speed required to perform DNNs, of a peak performance of 100 AI Petaflops. This will greatly accelerate the processing of deep neural networks and other compute-intensive AI workloads in order to improve training in areas such as image recognition, speech and natural language processing and computer vision. The TX-GAIA system comprises nearly 900 Intel processors and 900 Nvidia GPU accelerators. The new system is housed in a modular data center facility, co-developed with HPE and designed to speed deployment and reduce overall IT resources. It is located in Holyoke, Massachusetts, where it is powered by abundant green energy, and will go into production in the fall of 2019. We've seen strong industry demand for scalable performance to train higher volumes of AI that will advance science and engineering, and make breakthroughs across industries," said Bill Mannel, vice president and general manager, HPC and AI at HPE. "Our continued partnership with MIT Lincoln Laboratory Supercomputing Center extends the power of our HPC technologies to boost AI R&D and create new experiences."

Verizon's acquisition will include Jaunt's volumetric capture and machine learning technology. It's been a rocky couple of months for Jaunt XR. Late last year the software development startup conducted a series of layoffs to its staff as part of the companies transition from VR content to the development of AR technology with a focus on volumetric video capture. In November, the company began seeking buyers for its VR business and this past December company co-founder Arthur van Hoff announced his departure from the organization. Today, Jaunt announced the sale of all company assets to Verizon Communications Inc. as part of a new acquisition by the multimedia corporation.